Prior art relating to soft decision estimation units are, for example, reported in:
Citation 1: Joachim Hagenauer and Peter Hoeher, "A Viterbi algorithm with soft-decision outputs and its application", IEEE GLOBECOM'89 PA1 IEEE Cat. No. CH2682-March 1989, pp. 1680-1686, 1989 PA1 Citation 2: Jan-Eric Stjernvall, Bo Hedberg and Sven Ekenmark, "Radio test performance of a narrowband TDMA system", IEEE VTC'87, IEEE Cat. No. CH2429-September 1987, pp. 293-299, 1978. PA1 Citation 3: Gottfried Ungerboeck, "Adaptive Maximum-Likelihood Receiver for Carrier Modulated Data-Transmission Systems", IEEE Transactions on Communications, Vol. COM-22, No. 5, pp. 624-636, May 1974.
Prior art relating to a maximum-likelihood sequence estimation unit is, for example, reported in:
In high-speed digital communication, the transmission path characteristic thereof largely fluctuates with time due to frequency selectivity fading caused by mutipath transmission. In order to correctly restore originally transmitted symbols from a received signal which has been affected by the fluctuation and noise, an adaptive equalizer is often used. As the adaptive equalizer of this kind, the maximum-likelihood-sequence estimation-type equalizer (referred to as an MLSE equalizer hereinafter) disclosed in the Citation 1 or a decision-feedback-type equalizer employed in the Citation 2 is often used. In the maximum-likelihood sequence estimation unit which employs the MLSE equalizer, a received and digitized signal is passed through a matched filter which minimizes the influence of noise by changing the characteristics thereof in accordance with the transmission path characteristic so that the most likely transmitted symbol sequence is estimated from the output of the matched filter. A viterbi algorithm is often used as the maximum-likelihood estimation algorithm in this case, as disclosed in the aforementioned Citation 3.
Moreover, in high-speed digital mobile communication, usually error-correction codes such as convolutional codes are used to reduce transmission data error. That is, in case the convolutional code is employed as the error-correction code, symbols formed by converting the transmission data into convolutional codes for transmission are modulated at the time of transmission and at the time of reception they are demodulated from the modulation frequency band to a base band and then are estimated by the adaptive equalizer. Thereafter the convolutional codes are decoded to restore the transmitted data.
A viterbi algorithm is usually used for decoding the convolutional codes. Decoding the convolutional codes with the Viterbi algorithm is largely divided into two kinds, i.e., hard decision type and soft decision type, of which the soft decision type has better performance. In order to perform the soft decision type decoding, the output of the adaptive equalizer that is the input of the convolutional code decoding portion must be a soft decision.
As described in Citation 1, in case where the MLSE equalizer is used as the adaptive equalizer, it is necessary to employ the soft decision output type Viterbi algorithm as the maximum-likelihood estimation algorithm of the transmitted symbols in order to make the output of the adaptive equalizer a soft decision. In the soft decision output type Viterbi algorithm, a quantity representing the certainty of its corresponding transmitted symbol (known as reliability) is updated every time a path representing the transmitted symbol is determined. Accordingly, it requires a reliability memory arranged in a matrix of number of states.times.number of transmitted symbols, similarly to a path memory.
On the other hand, in case the decision feedback type equalizer employed in Citation 2 is used as the adaptive equalizer receiver, it is possible to obtain a soft decision basically by generating a signal before it is provided to a decision unit in the adaptive equalizer.
In a conventional soft decision estimation unit, however, in a case where the MLSE equalizer is used as the adaptive equalizer, it requires the reliability memory to be arranged in a matrix of number of states.times.number of transmitted symbols, and particularly when the maximum multipath delay time to be taken into consideration becomes long, the number of states which the soft decision output type Viterbi algorithm handles increases exponentially so that the capacity of the reliability memory becomes bulky. Moreover, there was also a problem that the number of processes for calculating the reliability became bulky.
In case a decision feedback type equalizer is employed as the adaptive equalizer, a soft dcision corresponding to an estimated transmitted symbol does not simply correspond to each bit generally in a modulation system in which a symbol is represented by a plurality of bits such as QPSK and QAM, which are used in high-speed digital mobile communication. Particularly in the case where interleaving is achieved on the transmission side to deal with a burst error, deinterleaving must be achieved at the output of the adaptive equalizer, but it is difficult to do so with conventional soft decision processes.